improvement: make data stream example about tabular classification

examples/rest-api/stream_data.py

@@ -2,28 +2,54 @@
 
 from openlayer import Openlayer
 
-# Prepare the config for the data, which depends on your project's task type. In this
-# case, we have an LLM project:
-from openlayer.types.inference_pipelines import data_stream_params
-
-# Let's say we want to stream the following row, which represents a model prediction:
-data = {"user_query": "what's the meaning of life?", "output": "42", "tokens": 7, "cost": 0.02, "timestamp": 1620000000}
-
 client = Openlayer(
  # This is the default and can be omitted
  api_key=os.environ.get("OPENLAYER_API_KEY"),
 )
 
-config = data_stream_params.ConfigLlmData(
- input_variable_names=["user_query"],
- output_column_name="output",
- num_of_token_column_name="tokens",
- cost_column_name="cost",
- timestamp_column_name="timestamp",
- prompt=[{"role": "user", "content": "{{ user_query }}"}],
-)
+# Let's say we want to stream the following row, which represents a tabular
+# classification model prediction, with features and a prediction:
+data = {
+ "CreditScore": 600,
+ "Geography": "France",
+ "Gender": "Male",
+ "Age": 42,
+ "Tenure": 5,
+ "Balance": 100000,
+ "NumOfProducts": 1,
+ "HasCrCard": 1,
+ "IsActiveMember": 1,
+ "EstimatedSalary": 50000,
+ "AggregateRate": 0.5,
+ "Year": 2020,
+ "Prediction": 1,
+}
 
+# Prepare the config for the data, which depends on your project's task type. In this
+# case, we have an Tabular Classification project:
+from openlayer.types.inference_pipelines import data_stream_params
+
+config = data_stream_params.ConfigTabularClassificationData(
+ categorical_feature_names=["Gender", "Geography"],
+ class_names=["Retained", "Exited"],
+ feature_names=[
+ "CreditScore",
+ "Geography",
+ "Gender",
+ "Age",
+ "Tenure",
+ "Balance",
+ "NumOfProducts",
+ "HasCrCard",
+ "IsActiveMember",
+ "EstimatedSalary",
+ "AggregateRate",
+ "Year",
+ ],
+ predictions_column_name="Prediction",
+)
 
+# Now, you can stream the data to the inference pipeline:
 data_stream_response = client.inference_pipelines.data.stream(
  inference_pipeline_id="YOUR_INFERENCE_PIPELINE_ID",
  rows=[data],